AN ABSTRACT OF THE THESIS OF
John E.B. Wofford for the degree of Master of Science in Fisheries Science presented
on December 17. 2003.
Title: Factors Influencing Within-Watershed Genetic Variation of Coastal Cutthroat
Trout in Camp Creek, Oregon.
Redacted for privacy
L
Abstract approved:
Robert E. Gresswell
Michael A. Banks
Because human land use activities often result in increased fragmentation of
headwater stream habitats, a better understanding of the effects of fragmentation on
the genetic heterogeneity of stream salmonids is useful for effective management.
We used eight microsatellites to examine the genetic structure of potamodromous
coastal cutthroat trout
(Oncorhynchus clarki clarki)
in Camp Creek, an isolated
headwater stream in western Oregon. Our objectives were to determine if coastal
cutthroat trout were genetically structured at fine spatial scales and to assess the
effects of natural and anthropogenic barriers on coastal cutthroat trout genetic
variation. Fish sampling occurred at 10 locations, and allele frequencies differed
significantly among all sampling sections. Dispersal barriers strongly influenced
coastal cutthroat trout genetic structure and were associated with reduced genetic
diversity and increased genetic differentiation. Results indicate that Camp Creek
coastal cutthroat trout exist as many small, partially independent populations
connected by low to moderate levels of gene flow. In headwater streams, increased
habitat fragmentation can result in genetic and demographic isolation leading to
reduced coastal cutthroat trout genetic diversity and compromising long-term
population persistence.
©Copyright by John E.B. Wofford
December 17, 2003
All Rights Reserved
Factors Influencing Within-Watershed Genetic Variation of
Coastal Cutthroat Trout in Camp Creek, Oregon
John E.B. Wofford
A THESIS
Submitted to
Oregon State University
In partial fulfillment of
the requirements for the
degree of
Master of Science
Presented December 17, 2003
Commencement June 2004
Master of Science thesis of John E.B. Wofford presented on December 17, 2003.
APPROVED:
Redacted for privacy
Co-Maj or Professor, representing Fisheries Science
Redacted for privacy
Co-Maj or Professor, representing Fisheries Science
Redacted for privacy
Head of Department of Fisheries and Wildlife
Redacted for privacy
Dean of GTaduate School
I understand that my thesis will become a part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Redacted for privacy
John
E.B. Wofford, Author
ACKNOWLEDGEMENTS
First and foremost, I would like to thank my major professor Bob Gresswell.
Bob has been an excellent teacher and mentor over the past several years,
encouraging me and supporting my decisions throughout my time at OSU. Thanks
for making learning so much fun. I would also like to extend appreciation to
Michael Banks. Michael took me into his lab, helped me struggle through months of
lab work, and always pushed me to "get done." It feels good now. Thanks to Doug
Bateman, Troy Guy, Nico Romero, Steve Hendricks, and Christian Torgersen for
providing such a great environment during my time in Corvallis. It's been a pleasure.
Without the help of Dave Jacobson and Daniel Gomez-Uchida, I would still be
running gels and cursing my PCR "bad luck." Dave and Daniel always answered my
stupid questions without laughing (at least to my face).
I'd also like to give credit to
Julia Jones for participating in my committee and maintaining an interest in this
research. This study was produced through the Cooperative Forest Ecosystem
Research (CFER) program, with funding provided by the USGS Forest and
Rangeland Ecosystem Science Center and the Oregon Department of Forestry. Last
and not least, thanks to Mom and Dad for love and support in all my endeavors.
TABLE OF CONTENTS
Pg
CHAPTER 1: INTRODUCTION
.2
CHAPTER 2: MATERIALS AND METHODS ................................................................. 5
Study Site and Sampling Procedures ............................................................................... 5
MicrosatelliteTyping .................................................................................................... 10
Genetic and Statistical Analysis .................................................................................... 11
Age-1+ Maximum Abundance and Ne Estimates .......................................................... 13
CHAPTER3: RESULTS ................................................................................................... 15
LociDiagnostics ............................................................................................................ 15
Temporal Stability and Family Sampling ...................................................................... 16
Gene Diversity and Differentiation ............................................................................... 17
Age-1+ Maximum Abundance and N Estimates .......................................................... 27
CHAPTER 4: DISCUSSION ............................................................................................ 28
CHAPTER 5: CONCLUSION .......................................................................................... 40
BIBLIOGRAPHY............................................................................................................. 41
APPENDICES ................................................................................................................... 47
LIST OF FIGURES
Figure
1.
Map of coastal cutthroat trout distribution and sampling
sections in the Camp Creek study area .................................................................... 6
2.
Color shaded relief map of coastal cutthroat trout distribution
and sampling sections in the Camp Creek study area ............................................. 7
3.
Mean gene diversity and allelic richness of 11 Camp
Creek populations in relation to the number of barriers
(anthropogenic and geomorphic) located downstream
of the respective population ................................................................................... 18
4.
Spatially explicit color depiction of Ots-209 genotype distributions
inCamp Creek ....................................................................................................... 19
5.
Spatially explicit color depiction of Ots-9 genotype distributions in
CampCreek ........................................................................................................... 21
6.
Simulation revealing the effect of small population size on
the rate allele loss due to genetic drift ................................................................... 23
7.
Neighbor-joining phylogram of 11 Camp Creek coastal
cutthroat trout populations using Cavalli-Sforza and
Edwards chord distance for 8 microsatellite loci ................................................... 26
LIST OF TABLES
Table
1.
Population structure and genotypic differentiation of
coastal cutthroat trout in Camp Creek .................................................................... 25
2.
Age-1+ Camp Creek coastal cutthroat trout
Ne
estimates ...................................... 27
LIST OF APPENDIX TABLES
Table
Page
A. 1.
Thermocycler profiles and PCR reactions ......................................................... 48
A.2.
Locus summary for Camp Creek coastal cutthroat trout
populations (including subsamples from age groups
andsample years) .............................................................................................. 49
Factors Influencing Within-Watershed Genetic Variation of Coastal Cutthroat
Trout in Camp Creek, Oregon
CHAPTER 1: INTRODUCTION
Proper conservation and management of aquatic species require accurate
descriptions of genetic structure and an understanding of the processes that produce
genetic heterogeneity. Investigations of genetic structure can provide insight into
large-scale historical and evolutionary phenomena (e.g. post-Pleistocene dispersal,
adaptive radiation) as well as recent population events (e.g. human induced
population bottlenecks). In salmonid species, studies have revealed complex
hierarchical or mosaic patterns of population organization at a variety of spatial scales
(Waples et al. 2001). These patterns are created by the interaction of genetic drift,
selection, inbreeding, and mutation, with the factors that influence gene flow, such as
landscape structure and dispersal behavior.
Lotic fish biologists have recently gained a greater appreciation of the
importance of dispersal in shaping genetic, demographic, and community structure
(Schiosser and Angermeier 1995, Maret et al. 1997). in salmonids, dispersal between
populations can provide demographic connections that can be critical for population
persistence (Morita and Yamamoto 2001) and recolonization following extirpation
(Hansen and Mensberg 1996, Gresswell 1999). In addition, dispersal provides
genetic linkages important for maintaining genetic variation that is critical to
preserving long-term adaptive potential of populations and species (Allendorf 1986).
From the perspective of a fish, headwater streams of western Oregon are
frequently fragmented by both natural (e.g. waterfalls and cascades) and
anthropogenic (e.g. culverts) barriers to dispersal. Many of these headwater streams
are inhabited by potamodromous coastal cutthroat trout (Oncorhynchus clarki clarki)
(Hooton 1997). Compared to other Pacific salmonids, potamodromous coastal
cutthroat trout are relatively sedentary, often exhibiting every life stage in a small
portion of a headwater stream (Trotter 1989). Although prior studies of anadromous
coastal cutthroat trout have revealed significant genetic heterogeneity among streams
(Wenburg and Bentzen 2001), little work has been done to quantify potamodromous
coastal cutthroat trout population structure or to address the effects of human
activities on coastal cutthroat trout genetic variation.
Headwater streams can be negatively affected by upsiope land uses, such as
road building and logging (Meehan 1991), and these actions can produce varying
effects on coastal cutthroat trout demographics (Murphy et al. 1986, Connolly and
Hall 1999). In order to further evaluate coastal cutthroat trout population responses to
habitat alterations and as a means to develop conservation goals, an improved
understanding of coastal cutthroat trout genetic structure is needed. Stream fish
conservation measures often fail because ecologists neglect to conduct research at
spatial scales that are appropriate for the organism of study and that are relevant to
human management of watersheds (Fausch et al. 2002). In an attempt to address this
problem, this study investigates genetic structure at an intermediate spatial scale,
where land use activities and coastal cutthroat trout populations occur in concert. We
used eight microsatellites to provide an assessment of coastal cutthroat trout genetic
differentiation within an isolated watershed. Our objective was to gain inference on
the conditions that influence coastal cutthroat trout population structure in stream
networks and to discuss the implications of these factors for management of coastal
cutthroat trout.
CHAPTER 2: MATERIALS AND METHODS
Study Site and Sampling Procedures
Camp Creek, a third-order stream in the Umpqua river basin of western
Oregon, was chosen for this study (Figure 1, Figure 2). The Camp Creek watershed
has a drainage area of 2200 hectares and is primarily composed of sedimentary rock
with riparian vegetation consisting of red alder (Alnus rubra), vine maple (Acer
circinatum), and big-leaf maple (Acer macrophyllum) (BLM 1995). Old-growth
Douglass-fir (Pseudotsuga menziesii) and red cedar (Thuja plicata) are present
throughout the riparian corridor. Although extensive logging has occurred on
tributaries and ridge tops, the watershed is currently managed as a late-successional
reserve (BLM 1995). The study sections of Camp Creek are isolated from migratory
fish by a 15-rn waterfall (barrier 1, Figure 1). Coastal cutthroat trout, reticulate
sculpin (Cottus perplexus), and longnosed dace (Rhinichthys cataractae) are the only
fish species present, and there are no records of fish stocking in the Camp Creek
basin.
During the summer of 2002, the watershed was surveyed in order to identify
geomorphic or anthropogenic barriers to fish passage. Two culvert barriers and four
geomorphic (waterfalls and bedrock cascades) barriers were identified byvisua1
assessments. Culverts were verified as fish passage barriers using FishXing v. 2.2
software (http://stream.fs .fed.us/fishxing/). An examination of historical aerial
photographs indicated that the two culvert barriers were installed in the mid to late
1950s. We were unable to date geomorphic barriers.
Waterfall (1)
Study
/ \\9
Site
/MSO
S
TI,
#..IA
54
44
Waterfall '2)
'
MS
4cv
Cascade (4)
N
1km
MS2
T4
43" 33' 34 N
123" 43' 12" W
1
I'
Culvert(5)
'\.,MS3
Waterfall (6)
IUT4
MS4
Figure 1. Map of coastal cutthroat trout distribution and sampling sections in the
Camp Creek study area. Sampling sections are identified by dashed lines. Solid bars
indicate barriers to fish passage. Captions associated with bars specify barrier types
and numbers in parentheses identify barriers. MS = mainstem, T = tributary, UT =
upper tributary.
/
7
Figure 2. Color shaded relief map of coastal cutthroat trout distribution and sampling
sections in the Camp Creek study area. Sampling sections are identified by colored
lines. Solid bars indicate barriers to fish passage. MS = mainstem, T tributary,
UT = upper tributary.
OREGOt4
Figure 2.
All genetic sampling locations were determined by tributary junctions or fish
passage barriers, excluding sites MS3 and MS4 (Figure 1, Figure 2). Sites MS3 and
MS4 were selected because it was important to sample the entire watershed, and no
tributaries or passage barriers occurred in the relatively extensive upper portions of
Camp Creek. In all, genetic sampling occurred at ten sites in the Camp Creek
watershed.
In August 2002, electrofishing crews began sampling at barrier 2 (Figure 1)
and proceeded upstream, sampling every pool and cascade in the fish-bearing
portions of the watershed using single-pass electrofishing without blocknets. For
estimates of adult maximum abundance (see below) we were primarily interested in
capturing age 1 + coastal cutthroat trout, so riffles were not sampled in the lower
portions of the basin because previous research has revealed that age-l+ coastal
cutthroat trout are almost exclusively found in pools, particularly during summer
months in watersheds dominated by sedimentary rock (Connolly 1996, Hicks and
Hall 2003). Due to additional research questions not addressed in this study,
upstream of barrier 3 (Figure 1), riffle habitats were also sampled.
Prior to release, trout were measured (nearest mm, fork length) and weighed
(nearest 0.1 g), and a small portion of caudal fin tissue (1.5 mm2) was collected. In
sections MS1, Ti, T2, and T3, up to ten fin clips were taken from trout (>50 mm in
length) in each 10 mm size class until 100 samples were collected or until sampling
crews reached the end of trout distribution. At site MSO and at all sites above barrier
3, fin clips were obtained from every captured trout. Fin tissue was stored in 2 ml
10
vials with a calcium sulfate desiccant (Indicating Drierite®). If needed, trout were
anesthetized in a solution of water and clove oil (Taylor and Roberts 1999). During
May 2003, we returned to the basin, and additional samples were collected at site
MS I using a hook and line: fish were also collected at site MSO for the first time.
Thus, nine sites were sampled solely in 2002, one site in 2003 (MSO), and one site in
both years (MS 1). Length-frequency histograms were used to identify trout age
groups.
Microsatellite Typjpg
Genomic DNA was extracted from small portions of tissue (0.5 mm2) in 200
iL of 5% Chelex®100 (Biorad) in 96-well PCR trays (0.2 tL) using a MJ research
PT-100 thermocycler. Tissue extracts were heated at 65CC for 3 hrs, boiled at 103CC
for 10 mm, and stored at 4CC. Eight microsatellite loci were used to characterize
coastal cutthroat trout genetic variability in the Camp Creek watershed. All forward
primers were labeled with fluorescent phosphoamidite (HEX, TET, or FAM). We
developed two multiplex sets: set A (Ots-209 and Ots-212 (Greig et al. 2003)) and set
B (One-i 02, One-i 03,
and
One-I 08
(Olsen et aT. 2000)). Ots-9,-i0 (Banks et aT.
1999) and Omy-1046 (Rexroad et al. 2002) were amplified individually in separate
PCR reactions. Thermocycler profiles and PCR reactions are listed in Appendix
Table A. 1. DNA fragments were fractioned by size on a 5% acrylamide gel and
visualized using a MJ Research Base-Station DNA fragment analyzer. Gels were
manually scored using MJ Bioworks Cartographer version 1 .2.3sg software. In order
11
to maximize sample sizes, we made second attempts at PCR reactions that failed to
produce scoreable products during initial processing.
Genetic and Statistical Analysis
Allele frequencies, number of alleles per locus, allelic richness, and estimates
of genetic distance (F) (Wright 1951), in the form of Weir and Cockerham (1984),
were calculated using FSTAT software (Goudet 1995). Significance of probability
values was evaluated by permutation procedures as implemented in FSTAT. Allelic
richness values were standardized by sample sizes using a rarefaction technique (El
Mousadik and Petit 1996, Petit et al. 1998). To test whether genetic divergence was
related to the geographic distance between populations, a Mantel test using 2000
randomizations was employed using FSTAT. Observed heterozygosity and gene
diversity (expected heterozygosity) were calculated using GENEPOP version 3.3
(Raymond and Rousset 1995). Tests for genotypic differentiation between all locuspopulation combinations were accomplished using exact tests and Marcov chain
methods as implemented in GENEPOP. Marcov chain methods from GENEPOP
were used to assess deviations from Hardy-Weinberg expectations and to evaluate
genotypic linkage disequilibrium between loci. Parameters for all Marcov chain
iterations included: dememorization number of 1000, 200 batches, and 1000
iterations. When appropriate, Bonferroni adjusted P-values were used for evaluating
statistical significance (Rice 1989).
Genetic distance measures, phylograms, and phylogram bootstrap values
(1,000 replicates) were generated using SEQBOOT, GENEDIST, NEIGHBOR, and
12
CONSENSE computer programs, respectively, as implemented in the PHYLIP
software package (Felsenstein 1991). Trees were edited using TREE VIEW (Page
1996).
When large numbers ofjuveniles were present in a sample, or when sampling
occurred over consecutive years, we tested for non-random sampling of family groups
(Hansen et al. 1997) and temporal stability of allele frequencies by examining
genotypic distributions, deviations from Hardy-Weinberg expectations, and genotypic
linkage disequilibrium between age groups/sample years within sampling locations.
If multilocus genotypic differences were encountered across age groups/sample years
at a sample location, the respective subsamples were considered to be separate
populations. Within age-0 samples, deviations from Hardy-Weinberg expectations or
multiple failures of tests for genotypic linkage disequilibrium (>10% failures per
population (Banks et al. 2000)) were viewed as evidence of family sampling.
Three-dimensional visualizations of each population were accomplished using
factorial correspondence analysis (FCA) as performed by GENETIX v 4.04. Missing
genotypes can impair the interpretation of FCA results, so prior to this analysis,
genotype data from each population was resampled in order to produce 500 new
individuals per population with complete genotypes using WHICHLOCI software
(Banks et al. 2003). This program generates new populations of individuals with
complete genotypes whose allele frequencies are concordant with original
populations.
13
Spatially explicit depictions of coastal cutthroat trout genotypes were
produced using ArcScene software with techniques described in Torgersen, et al. (In
press.). Using WHICHLOCI software, genotypes were simulated for all captured
coastal cutthroat trout in frequencies that were concordant with observed allele
frequencies. Relative abundance data were not available for the MSO population, so
this population was excluded from Figures 4 and 5
Genetic simulations were executed using EASYPOP 1.7 (Balloux 2001) and
were performed under the following conditions: random mating, free recombination
between loci, a single step mutation model (rate of 0.001), eight loci with a maximum
of eight alleles (average number of alleles per locus in Camp Creek basin). Variance
estimates were calculated from 200 replicates of each simulation.
Age-1+ Maximum Abundance and
Ne
Estimates
Coastal cutthroat trout relative abundance data allowed us to estimate
maximum abundance for age-l+ trout in 9 of the 10 sampling locations. Age-1+
maximum abundance was estimated by dividing the number of age-1+ fish captured
by a capture probability of 0.5. Although electrofishing capture probabilities in
headwater streams of western Oregon approach 0.9 (Bateman D.B., unpublished
data), a capture probability of 0.5 was used as a conservative measure.
These maximum abundance values were used to estimate coastal cutthroat
trout effective population sizes
(Ne). Allendorfet al. (1997) suggested Ne/N
0.2 as
an approximation for wild populations of Pacific salmon, where N represents the total
population size per generation, estimated by multiplying the number of spawning
14
adults by mean generation time. The Ne/N = 0.2 approximation has been validated for
a potamodromous salmonid species (Rieman and Allendorf 2001). The
Ne
of each
coastal cutthroat trout population was estimated with this approximation, using age1+ maximum abundance as a conservative measure of spawner abundance and
assuming a mean generation time of two years (Nicholas 1978).
Using aerial photography, it was possible to estimate when the Ti population
was isolated from the Camp Creek stream network, and thus, we were able to
evaluate Ne of Ti using genetic techniques employing moment and likelihood
methods of MLNE software (Wang and Whitlock 2003). We assumed that prior to
culvert installation the Ti population had allele frequencies that were similar to the
current MS 1 population. The MS 1 population appears to be the largest population
(above barrier 2) in Camp Creek and thus is expected to have changed the least in the
last 40 years. Although it is unlikely that allele frequencies in the Ti population were
identical to those in the MS 1 population, the small
values between MS I and
nearby T2 and T3 suggest that allele frequencies were probably similar and
appropriate for a coarse estimate of N.
15
CHAPTER 3: RESULTS
Loci Diagnostics
Sample sizes for individual populations ranged from 31
(One-i 02
in T2) to
118 (Omy-i 046 in MS3) and averaged 68 (Appendix Table A.2). All eight of the
microsatellite loci analyzed were polymorphic in Camp Creek coastal cutthroat trout.
Across all populations, the number of alleles per locus ranged from 3 (Ots-209) to 11
(One-i 02)
with an average of 8.0 alleles per locus, although upstream of barrier 2, the
average number of alleles per locus dropped to 4.1. A total of 20 private alleles (rare
alleles observed in a single population) were documented, at least one private allele
(mean frequency = 0.03 8) occurred at each locus. Seventeen of these private alleles
were found in the MSO population; upstream of barrier 2, three private alleles
(average frequency 0.013) occurred in two loci
(One-I 02
and
One-i 08)
with three
populations (T3, T4, and MS3) each containing one private allele.
Deviations from Hardy-Weinberg equilibrium were observed in 9 of 82 (10.9
%) possible tests within loci across populations (a = 0.05/11 = 0.0045) and in 10 of
82 (12.2 %) possible tests across loci within populations (a = 0.05/8 = 0.00625).
Heterozygote deficits were spread among six populations and five loci with no more
than three deficits at any one locus or population. A total of 15 of 267 (5.6%)
possible loci combinations failed tests for genotypic linkage equilibrium (a = 0.05/28
= 0.0018). These failures occurred across 10 loci combinations with no more than 3
at any one loci pair.
16
Temporal Stability and Family Sampling
We were able to test for temporal stability of allele frequencies and sampling
of related individuals because of two sampling issues: 1) The MS1 sample location
was sampled during consecutive years, and (2) length-frequency histograms indicated
that several samples contained enough age-0 trout for reasonable sub-sampling (>10
age-0 trout at sites MS2, T4, UT4, MS3, and MS4). Tests for genotypic
differentiation between sample years in the MS 1 location revealed no significant
temporal changes in allele frequencies (a = 0.05/8 = 0.0063). Therefore, the MS1
samples from 2002 and 2003 were pooled into one sample. Comparisons across age
groups at other sample locations indicated that three populations (MS2, MS4, and T4)
contained genotypic differences between age-0 and age-1+ trout. In the MS2 and
MS4 populations, age groups differed at a single locus
(Omy-1046 and Ots-JO,
respectively), but in the T4 location, age groups differed at five of seven possible loci
comparisons (Ots-212, Omy-1046, Ots-lO,
One-102, One-103)
(a = 0.05/8 = 0.0063).
Because of the significant multilocus differences between age groups, samples from
the T4 location were split into two samples (one containing age-0 trout, the other
composed of age-1+ trout).
Tests for genotypic linkage disequilibrium and Hardy-Weinberg expectations
within age groups indicated potential sampling of many highly related individuals in
the MS4 age-0 population. In the MS4 age-0 samples, 5 of2l (24%) possible loci
comparisons failed tests for genotypic equilibrium (a = 0.05/28 = 0.0018). In
addition, three of seven polymorphic loci in the MS4 age-0 population differed
17
significantly from random mating proportions (a = 0.05/8 = 0.00625). Poor
discriminatory power among loci precluded the correction of age-0 MS4 sample for
relatedness (Banks et al. 2000), therefore, all age-0 fish were removed from the MS4
sample before further analysis. None of the other age-class subsamples revealed
genotypic linkage disequilibrium at greater than 10% of possible comparisons, and
only one population showed evidence for deviation from Hardy-Weinberg
expectations. This deviation occurred at a single locus (Omy-1046) in the UT4 age 1+
population (a = 0.05/28
0.0018 and a
0.05/8 = 0.00625 respectively).
Final data adjustments, therefore, included pooling the 2002 and 2003 samples
from the MSI location, splitting the T4 sample into two samples of age-0 and age-1+
trout (resulting in samples T4-0 and T4-1), and removing all age-0 fish from the MS4
sample (resulting in sample MS4-1). Following these data adjustments, heterozygote
deficits decreased within loci across populations (10.9% to 6.1%) and within
populations across loci (12.2% to 7.3%), and total genotypic linkage disequilibrium
decreased from 5.6% to 4.9%.
Gene Diversity and Differentiation
Mean within-population gene diversity was 0.50, and mean allelic richness
was 3.96. Gene diversity and allelic richness tended to decrease with increasing
genetic isolation (Figure 3, Appendix Table A.2). Tributaries that were connected
with mainstem habitats tended to have relatively high levels of ailelic richness and
gene diversity, and low values were associated with barriers to gene flow (Figure 4,
II
Figure 5, Appendix Table A.2). Genetic drift dramatically impacted the isolated
tributaries, resulting in occasional increased frequencies of rare genotypes (associated
with basin-wide rare alleles) (Figure 4) and a loss of genetic diversity (Figure 5).
Simulations reveal the strong influence of genetic drift on the loss of alleles in small
populations (Figure 6).
1.0
10
0.8
8
rdD
(I,
0)
0.6
6
0)
0)
4
0)
0.2
0.0
0
1
2
3
5
Number of Barriers Isolating Population(s)
Figure 3. Mean gene diversity and allelic richness of 11 Camp Creek populations in
relation to the number of barriers (anthropogenic and geomorphic) located
downstream of the respective population. None of the populations in Camp Creek are
isolated by four barriers. Bars show ± 1 standard error.
Figure 4. Spatially explicit color depiction of Ots-209 genotype distributions in
Camp Creek. Heights of bars represent the abundance of coastal cutthroat trout
captured at a location and color codes indicate the probable genotypes of captured
trout. Note the unusual distribution of genotypes associated with the isolated
tributaries.
Figure 4.
21
Figure 5. Spatially explicit color depiction of Ots-9 genotype distributions in Camp
Creek. Heights of bars represent the abundance of coastal cutthroat trout captured at
a location and color codes indicate the probable genotypes of captured trout. Note the
reduced number of genotypes (associated with a reduced number of alleles) in the
isolated tributaries.
Figure 5.
t'J
23
10
8
riD
I)
0
4
2
0
0
10
20
30
40
50
60
70
80
90
100
Generation
Figure 6. Simulation revealing the effect of small population size on the rate of allele
loss due to genetic drift. At generation 0, the simulated isolated population contained
8 alleles at maximum variability. Bars show ± 1 standard deviation.
When comparing each population-pair across loci, significant genotypic
differences were detected in 369 of 433 (85.2%) possible tests (a = 0.05/8
0.00625). Genotype distributions differed between population-pairs at a majority of
loci (mean = 6.7 loci; range = 2 loci - 8 loci) (Table 1). Genetic differences between
populations were also evident from pairwise
0.0 14 to 0.393) (Table 1). The largest
estimates (mean
0.124; range =
values were associated with the two fully
24
isolated populations (Ti, UT4). The mean pairwise
value for populations that
were not separated by barriers (0.062) was significantly different from the mean
between barrier-separated populations (0.144) (Mann Whitney, P < 0.01). This result
was related to the effects of the tributaries that were completely isolated from the
remainder of the stream network (sites Ti and UT4); excluding these populations,
differences in mean pairwise
values between separated (0.085) and connected
(0.062) populations were not significant (two sample T-test, P> 0.05). No significant
relationship was observed between genetic distance and geographic distance and
therefore migration rates were estimated assuming an island model. According to
Wright's island model
[F
1I(4Nm + 1),
where Nm represents the effective number
of migrants between populations per generation] (1931), a mean
of 0.124
translates into a migration rate between populations of approximately 2 effective
migrants per generation. After excluding populations Ti and UT4, the estimated
migration rate between populations was approximately 4 effective migrants per
generation (range = < I - 12 effective migrants).
Despite significant differences in allele frequencies among populations,
factorial correspondence analysis revealed extensive overlap of populations. This
overlap was not a result of compression of the FCA due to outlying populations,
because after removing the three most differentiated populations (Ti, UT4, and MS41), the overlapping patterns remained. A neighbor-joining phylogram of CavalliSforza and Edwards chord distance illustrates the influence of fish passage barriers on
coastal cutthroat trout genetic structure in Camp Creek (Figure 7). Phylogram
25
coastal cutthroat trout genetic structure in Camp Creek (Figure 7). Phylogram
organization is related to the spatial location of mainstem barriers, dividing the
phylogram into the lower (MSO), middle (MS 1, Ti, T2, T3) and upper watershed
(MS2, MS3, T4-O, T4-1, UT4, MS4). The large divergence of tributary populations
Ti and UT4 is associated with passage barriers.
Table 1. Population structure and genotypic differentiation of coastal cutthroat trout
in Camp Creek. Values above the diagonal represent pairwise Ft values, and
numbers below the diagonal represent the number of loci (out of 8) that revealed
significant genotypic differentiation betweem populations (a 0.05/8 = 0.00625).
values were significant at P<0.00l
Following Bonferroni adjustments, all
excluding the T3/MSI comparison (significant at P<0.Ol).
Sample Section
Sample
Section
MSO
MSO
MS4-I
MS1
TI
T2
T3
MS2
T4-0
T4-1
UT4
MS3
0.04
0.19
0.06
0.03
0.07
0.10
0.10
0.19
0.06
0.09
0.22
0.03
0.01
0.04
0.08
0.08
0.19
0.05
0.09
0.25
0.24
0.26
0.37
0.33
0.39
0.23
0.32
0.04
0.08
0.11
0.10
0.21
0.04
0.10
0.05
0.09
0.09
0.21
0.04
0.06
0.05
0.03
0.15
0.03
0.06
0.12
0.20
0.09
0.15
0.20
0.07
0.06
0.12
0.17
-
MSI
8
TI
8
8
T2
8
6
7
T3
6
2
7
4
MS2
8
5
8
7
5
T4-0
8
8
8
7
7
4
T4-I
7
6
8
8
6
5
-
-
5
-
UT4
8
8
6
8
8
8
7
7
MS3
8
7
6
8
4
5
7
7
8-
MS4-1
8
7
8
8
7
5
4
4
7
0.06
7
-
26
Ti
46
JT4
0.1
Figure 7. Neighbor-joining phylogram of 11 Camp Creek coastal cutthroat trout
populations using Cavalli-Sforza and Edwards chord distance for 8 microsatellite
loci. Numbers at nodes represent the percentage of bootstrap simulations that support
the associated groups (1000 replicates). Gray bars represent fish passage barriers,
numbers in bars identify barriers (as depicted in Figure 1), and arrows indicate
potential downstream directional gene flow.
27
Age-l+ Maximum Abundance and
Ne
Estimates
Maximum abundance estimates for age-i + coastal cutthroat trout revealed
small populations within sampling locations (Table 2). Fewer than 4% of the
captured age-i + trout were found in riffle habitats, supporting the assumption that the
majority of age-l+ trout would be found in pools and cascades. The Ne estimate for
the Ti population using genetic data was 27 (95% C.I. = 19-40), lower than the
approximation derived from the relative abundance data (Ne
65). The use of a
conservative capture probability could lead to overestimation of maximum spawner
abundance (and thus an overestimate of N) and might explain the difference between
the genetic and maximum abundance estimates of Ne in the Ti population.
Table 2. Age-1+ Camp Creek coastal cutthroat trout Ne estimates. Maximum
abundance data was not estimated for the MSO population.
apopulation estimate using genetic data.
Sampling Section
Age-i+
MSO
MS1
maximum
abundance
NA
222
Ti
T2
T3
MS2
T4
UT4
MS3
162
72
80
72
68
68
102
MS4
70
Ne estimate
NA
89
65(27a)
29
32
29
27
27
40
28
28
CHAPTER 4: DISCUSSION
This study provides the first attempt at describing fine-scale within-watershed
population structure of potamodromous coastal cutthroat trout. All of the examined
populations were genetically distinct, despite small distances separating populations.
High
values, reduced heterozygosity, and a loss of alleles associated with barriers
to fish passage demonstrated the strong genetic effects of population isolation.
However, moderate genetic structure was also observed in continuous sections of
stream at micro-scales (<200 m). It appears that coastal cutthroat trout in Camp
Creek are structured as partially independent populations that are connected with low
to moderate amounts of gene flow. Where dispersal is possible, this gene flow is
adequate for preserving genetic diversity (at least over short time periods), but gene
flow is not sufficient to maintain genetic homogeneity throughout the watershed.
Small effective population sizes, in conjunction with population isolation related to
fish passage barriers, creates a situation where random genetic drift strongly impacts
genetic heterogeneity.
In salmonid genetic studies, collecting samples (particularly from juveniles)
over a short section of stream can lead to the collection of many highly related
individuals, creating biased results and increasing deviations from Hardy-Weinberg
expectations (Allendorf and Phelps 1981, Hansen et al. 1997). This has proved to be
a problem in prior genetic studies of coastal cutthroat trout (Wenburg et al. 1998). If
a large number ofjuveniles are sampled, it is advisable to identify and selectively
29
remove related individuals prior to data analysis (Banks et al. 2000). However, in
populations with low polymorphism, or if few loci are examined, it can be difficult to
acquire the necessary statistical power for an analysis of relatedness. In this study,
we assessed departures from Hardy-Weinberg equilibrium and linkage disequilibrium
within age groups as a means of addressing the potential sampling of related
individuals. Although this technique is not as effective as a relatedness analysis,
relatively few loci are required and the information content of the loci is essentially
irrelevant. Subsequent data adjustments allowed us to substantially reduce departures
from HWE and improved the validity of our results.
Fine-scale population structure in streams has been noted in other salmonid
species including bull trout (Salvelinus conJluentus) (Spruell et al. 1999, Neraas and
Spruell 2001), brook trout (Salvelinusfontinalis) (Herbert et al. 2000), and brown
trout (Salmo trutta) in Europe (Carlsson et al. 1999, Carlsson and Nilsson 2000,
2001). Reproductive isolation, due to precise natal homing or barriers to fish
movement, is frequently cited as the cause of fine-scale genetic heterogeneity in trout
populations. However, significant allelic divergence can occur even with
considerable gene flow between populations (Allendorf and Phelps 1981). This fact
is accentuated in small populations because divergence owing to genetic drift occurs
at a rate that is inversely proportional to population size (Allendorf and Phelps 1981).
Thus, estimates of effective population size can help provide a context for
interpreting genetic distance measures (Moritz et al. 1995). In small populations,
short term variations in dispersal rates coupled with overriding forcing by genetic
30
drift can lead to significant genetic divergence even if large amounts of genetic
exchange occurs over longer time scales.
Dispersal rates in salmonids can be highly variable (Rieman and Dunham
2000), and this variation can increase temporal instability of allele frequencies in
trout populations (Spruell et al. 1999). Even so, because different age classes within
sampling sites were most often homogenous, much of the genetic structure observed
in Camp Creek appears to be stable (at least over a few generations). Nevertheless,
the T4 population had substantial evidence for temporal instability. Pairwise
comparisons indicated that fry in the T4 location were not genetically similar to any
other population in Camp Creek, and considering the extensive sampling that
occurred in the watershed, it is unlikely that the fry were offspring from some
unknown population. In addition, the age-0 T4 population met Hardy-Weinberg
expectations, and there was no evidence for linkage among loci, thus ruling out a
strong family sampling effect. Although it is possible that genetic drift contributed to
the heterogeneity between age groups in T4, the large
age groups, in conjunction with the relatively small
value observed between the
value between the age-0 T4
population and the MS2 population, indicates that the temporal variation in the T4
cutthroat trout likely occurred as a result of recent gene flow into T4 from the nearby
MS2 population and not as a result of genetic drift.
The unusual distribution of private alleles as well as the extensive population
overlap in the FCA provides additional evidence for demographic connections among
the Camp Creek populations. Very few private alleles were documented above
31
barrier 2, indicating repeated founder events or extensive gene flow in the upper
portions of the watershed (Slatkin 1985). Based on
values, dispersal rates between
populations ranged from low to moderately high. However, because of the strong
effect of genetic drift on the small Camp Creek populations, inferring dispersal rates
from
values might lead to underestimation of movement rates. Even so, these
estimates of dispersal and genetic divergence apply only to recent time periods.
Fluctuations in habitat quality and population densities along with the formation or
elimination of dispersal barriers can alter salmonid migration rates (Rieman and
Dunham 2000), and genetic divergence or convergence can occur over extremely
short time scales (one or two generations, in this case, two to four years) with small
effective population sizes.
Recent studies of watershed-scale movement of coastal cutthroat trout in
Camp Creek also suggest that gene flow may be limited. Hendricks (2002) reported
that most coastal cutthroat trout in Camp Creek moved at the local spatial scale (10
m); however, some individuals moved from 100 to 1 000m, distances greater than
those separating many populations in this study. Although it is impossible to
determine the genetic contribution of individual fish from mark-recapture data, trout
movement in Camp Creek was most frequent during spawning seasons, suggesting
that movement was reproductively motivated. Such highly mobile individuals may
succeed in partially counteracting the effects of genetic drift and inbreeding in these
populations.
Current levels of gene flow in Camp Creek do not appear to be adequate for
maintaining homogeneity in allele frequencies, but where migration was possible,
persistence of alleles and maintenance of relatively high levels of gene diversity were
noted. In contrast, reduced genetic diversity and allelic richness were directly related
to the degree of habitat fragmentation and ensuing genetic isolation. Genetic theory
predicts that when migration between small populations is restricted by dispersal
barriers, the subsequent loss of genetic diversity can occur rapidly (Hart! and Clark
1997). Indeed, the Ti population, which has only been isolated for 45 years (as of
2003), had roughly 50% fewer alleles than nearby tributaries with mainstem
connections. Populations in isolated tributaries TI and UT4 underscored the
importance gene flow from either upstream or downstream; with no potential
immigration, coastal cutthroat trout in these tributaries exhibited the lowest gene
diversity, lowest allelic richness, and the highest degree of genetic divergence. The
rapid loss of alleles and increased genetic differentiation in these isolated populations
is likely an effect of genetic drift and inbreeding, speeded by small effective
population sizes and short generation times of coastal cutthroat trout.
The effect of barriers on the genetic heterogeneity of coastal cutthroat trout in
Camp Creek appears to be largely dependent upon the extent of isolation caused by
the respective barrier. Just as the spatial organization of critical habitats affects the
distribution of populations, the spatial location of dispersal barriers influences the
genetic consequences of isolation. When barriers are located on small tributaries,
individual populations are isolated. In contrast, barriers in the mainstem tend to
33
isolate many populations. Gene flow among multiple populations increases long term
effective population sizes thus reducing the effects of genetic drift and inbreeding.
This might explain the relatively low F1 values that occurred among most mainstem
populations and the large F1 values associated with the isolated tributaries. Although
mainstem barriers were related to decreased genetic diversity, the fact that they did
not have a major effect on allele frequencies suggests that some downstream gene
flow is occurring in the mainstem of Camp Creek.
Certainly, divergence between below- and above-barrier populations will be
dependent not only upon the amount of downstream dispersal, but also on the time
since isolation and the respective demographic histories of the individual populations.
Considering the small effective population sizes in Camp Creek, even if mainstem
barriers were formed relatively recently, with little gene flow, greater divergence than
was observed would be expected (Allendorf and Phelps 1981). Other genetic studies
suggest that the degree of divergence among coastal cutthroat trout populations
separated by natural barriers is variable (Griswold et al. 1997). It has been noted that
downstream movement of salmonids in isolated headwaters would be limited by
strong genetic selection against downstream dispersal. Nevertheless, downstream
movement of above-falls individuals has been documented (Johnston 1982, Northcote
and Hartmann 1988, Northcote 1992, Hendricks 2002). In the headwaters of Camp
Creek, these emigrants appear to be contributing genetically to downstream
populations. Because our sampling was limited above a barrier to anadromous fishes,
34
this study does not necessarily apply to genetic interactions between anadromous and
potamodromous coastal cutthroat trout.
Partially independent populations that exist in patchy, dynamic habitats can be
loosely characterized as metapopulations (Hanski and Simberloff 1996).
Metapopulation theory conceptualizes the dynamics of population extirpation and
recolonization, and substantial attention has been focused on integrating
metapopulation concepts with lotic fish biology (Schlosser and Angermeier 1995).
Although there is little support for a simple, generalizable model of metapopulations
for salmonids, evidence does suggest that metapopulation processes do play a role in
regulating salmonid populations (Rieman and Dunham 2000). We did not document
an extirpation and recolonization event, but based on evidence of intermittent gene
flow among populations, metapopulation structure is likely in some portions of Camp
Creek.
Considering the small spatial scale of the watershed and the relative success of
coastal cutthroat trout in headwater habitats, it appears that following an extirpation
event available habit would be quickly recolonized. Rapid recolonization of
accessible habitat is common in salmonid population (Gresswell 1999). The reduced
heterozygosity and low allelic richness observed in the upper portions of Camp Creek
(MS4-1), despite the absence of definitive fish barriers, may be an indication that the
population was recently founded by individuals from downstream populations.
However, high gradient cascades in this portion of the basin could limit upstream
gene flow that would be necessary for maintaining genetic variation even without a
35
bottleneck or extirpation event. Indeed, there are many geomorphic structures, such
as cascades, that could function as dispersal filters, restricting but not eliminating
gene flow among populations (Kocik and Ferreri 1998). Following floods or other
disturbance events, new filters or barriers may be created, or existing structures may
be altered, thus affecting future dispersal rates. Habitat changes can also influence
dispersal through indirect processes related to density dependent movements or
fluctuations in critical habitat availability (Rieman and Dunham 2000). In this way,
genetic variability reflects environmental variability and future population structure is
linked with past alterations of habitat.
Coastal cutthroat trout in the Pacific Northwest occupy headwater streams that
are frequently fragmented by geomorphic dispersal barriers. Although the spatial
distribution and characteristics of these features have not been adequately quantified,
field observations of over 50 watersheds in western Oregon indicate that natural
barriers are rarely found at tributary junctions (Gresswell R.E., unpublished data).
Tributaries provide a major function as sediment delivery systems and the deposition
of alluvium at tributary junctions likely reduces the probability of barrier formation as
well as the likelihood that a barrier could persist over time. However, road
construction along the narrow terraces of headwater streams is frequently associated
with culvert installations that impede fish movements into small tributaries. The
watershed scale demographic effects of this fragmentation are unknown; however, in
Camp Creek, isolation of small tributaries quickly resulted in a loss of upstream
36
genetic variation, thus reducing the spatial distribution of individual alleles in the
stream network and restricting more of the genetic diversity to mainstem habitats.
The spatial distribution of alleles in Camp Creek is especially important
because the watershed is small and stochastic disturbances, such as fires or debris
flows, could negatively affect a significant portion of the fish-bearing habitats. When
allelic spatial distributions are reduced, the probability of a permanent loss of genetic
diversity and associated phenotypic diversity following a disturbance, increases. A
loss of genetic variation can negatively affect the ability of a population to adapt and
persist in the face of environmental change (Allendorf et al. 1987), and without a
better understanding of the relationship between genetic diversity and fitness,
maintaining genetic diversity should be a driving objective for salmonid conservation
(Wang et al. 2002).
In the absence of extirpations, fragmented populations can actually retain
higher genetic diversity than a single population of the same total size (Kimura and
Crow 1963). As a result, zoo managers often intentionally fragment captive
populations of endangered species (Margan et al. 1998, Woodward et al. 2002). This
technique can work well when individual populations have low extirpation risks (as in
many captive populations), but in natural situations with small populations, habitat
fragmentation often increases extirpation risks (Morita and Yamamoto 2001) and
usually results in genetic (Maruyama and Kimura 1980), demographic (Pulliam and
Dunning 1997), and ecological degradation (Noss and Csuti 1997). From a genetic
37
perspective, intentional fragmentation of wild populations has little conservation
value and should not be advocated (Chambers 1995, Kruse et al. 2001).
Nevertheless, the existence of more than 250 coastal cutthroat trout
populations above natural barriers in western Oregon (Gresswell et al. 2003) suggests
that coastal cutthroat trout are at least partially adapted to fragmented stream habitats.
At a range-wide spatial scale, this fragmentation potentially contributes to coastal
cutthroat trout genetic diversity. However, at small spatial scales, fragmentation will
generally have long-term negative consequences on the genetic variation of individual
assemblages of coastal cutthroat trout. Despite the risks involved with genetic
isolation, when introduced species threaten native salmonid populations with
hybridization and competitive exclusion, intentional isolation is increasingly viewed
as an appropriate measure for conservation (Kruse et al. 2001, Novinger and Rahel
2003). Evidence suggests that managers should consider intentional isolation only
when other conservation strategies have been unsuccessful, and it is important to
evaluate trout population sizes, local disturbance regimes, and habitat connectivity in
conjunction with population genetic characteristics when determining the potential
effects of isolation (Hilderbrand and Kruse 2000, Novinger and Rahel 2003).
Substantial genetic structure has been observed at multiple levels of coastal
cutthroat trout spatial distributions: range-wide (Williams, 1.11. 2003, personal
communication), among watersheds (Wenburg et al. 1998, Wenburg and Bentzen
2001), large watersheds (Zimmerman 1996, Wenburg and Bentzen 2001), and small
watersheds (Griswold et al. 1997). In resource management, genetic structure is often
used to determine the appropriate spatial scale for management actions (Allendorf et
al. 1987). Independent management of genetically distinct populations is predicated
on the assumption that genetic structure represents demographic independence and
the potential for local adaptations (Carvaiho 1993, Moritz et al. 1995). Although
significant genetic structure was observed in the Camp Creek basin, much of this
fine-scale heterogeneity is likely derived from the effects of genetic drift and is not a
result of natural selection and complete reproductive isolation. Although salmonids
can develop local adaptations at small spatial scales (Gresswell et al. 1997, Olsen and
Vollestad 2001, Koskinen et al. 2002, Olsen and Vollestad 2003), the apparent gene
flow and metapopulation structure among populations in Camp Creek suggests that
management should be focused at larger spatial scales. For proper conservation of
salmonid metapopulations, managers must focus not only upon individual populations
and critical habitat areas, but also, and perhaps more importantly, on reestablishing
linkages among tributary and mainstem populations, thereby obtaining the
demographic and genetic benefits of population connectivity (Gresswell 1997).
Although many populations in Camp Creek have relatively low genetic
diversity, these data do not directly address the probability of future persistence, nor
do they suggest that coastal cutthroat trout are resilient to the negative effects of
genetic homogeneity. The fact that there are many isolated streams with adequate
habitat that do not support fish suggests that at some temporal scale, isolation leads to
extirpation, and this may be related to the process of genetic degradation.
Furthermore, in fragmented habitats, demographic and environmental stochasticity
39
alone can lead to extirpation of salmonid populations (Morita and Yokota 2002).
Indeed, at short temporal scales, demographic and environmental variability may be
more important to population persistence than genetic heterogeneity.
CHAPTER 5: CONCLUSION
Significant fine-scale genetic structure was noted in coastal cutthroat trout
within a 3rd order headwater stream of western Oregon. Available evidence suggests
that this structure was a result of the combined effects of genetic drift and population
isolation. When passage barriers were present, gene flow was reduced and the effects
of random drift and inbreeding resulted in a loss of genetic variation and increased
population differentiation. At landscape scales, barriers may have a potential role in
maintaining coastal cutthroat trout genetic diversity, and it is not recommended that
natural barriers be modified for fish passage. However, isolation of small tributaries
as a result of land management activities has few beneficial consequences for coastal
cutthroat trout because of negative genetic, and likely demographic, effects at all
spatial scales. These data suggest that habitat fragmentation in headwater streams can
lead to a rapid reduction of genetic diversity that is detrimental to coastal cutthroat
trout population persistence and long term conservation planning. For successful
conservation of coastal cutthroat trout, managers should be mindful of the importance
of habitat connectivity and strive to maintain or restore trout dispersal pathways in
streams.
41
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47
APPENDICES
Table A. 1. Thermocycler profiles and PCR Reactions.
Multiplex
Thermocycler Steps
Loci
1
Ots209
A
Ofs212
1 cycle ©
(94°C for 3
mm)
PCR
3
2
32 cycles @ (94°C for 30$ + 63°C
for 20s + 72°C for 30s)
1
cycle © 72°C
for 10 mm
One-
One-
see Olsen et al. 2000b
103
1046
NA
Ots-9
1 cycle @
(94°C for 3
mm)
1 cycle ©
(94°C for 3
mm)
Ots-
NA
10
2.0
0.125
0.5
0
2.0
0.200
0
0.5
0.2
1.5
0.200
0.5
0
02
2.0
0.125
0.5
0
0.2
2.0
0.125
0.5
0
buffer (IJL)
0.075
0.5
0.025
108
Omy
TrisIKCl
buffer (pL)
dNTPs
(mM)
Promega® PCR
(mM)
0.025
One-
NA
MgCl2
0.025
102
B
Primer
(mM)
Reagentsa
1 cycle @
(94°C for 3
mm)
29 cycles @ (94°C for 30s + 55°C
for 20s + 72°C for 30s)
1 cycle
32 cycles © (94°C for 30s + 63°C
for 20s + 72°C for 30s)
1 cycle
32 cycles © (94°C for 30s + 63°C
for 20s + 72°C for 30s)
1 cycle
© 72°C
for 10 mm
© 72°C
for 10 mm
@ 72°C
for 10 mm
reactions were conducted in 5 pL volumes using 1 pL genomic DNA template, 0.25 pL BSA PCR enhancer, 0.025 U Taq DNA polymerase
S.L. Wilson, E.J. Kretschmer, K,C. Jones, and J.E. Seeb. 2000. Characterization of 14 tetranucleotide microsatellite loci derived from sockeye
salmon. Molecular Ecology 9:2185-2187
°All PCR
bOlsen
J.B.,
00
49
Table A.2. Locus summary for Camp Creek coastal cutthroat trout populations
(including subsamples from age groups and sample years). A number of alleles
with allelic richness in parentheses, R = allele size range, S = most common allele, F
= frequency of most common allele, Gd = gene diversity (expected heterozygosity),
= observed heterozygosity, N = sample size (number of successfully genotyped
individuals).
a
Indicates populations that were used in the final analysis.
Locus
Ots-9
Ots-209
Ois-212
Omy-1046
Ots-JO
One-i 02
One-108
One-103
Mean
A
7(5.4)
5(4.67)
6(5.56)
8(6.09)
6(5.01)
10 (8.74)
10(7.88)
8 (6.14)
7.63 (6.19)
R
107-125
139-159
107-155
95-147
172-198
200-256
155-251
114-154
Sample Section
MS0
MSI (2001,
2002)
MSI (2002)
MSI (2003)
TP
T2'
5
113
147
107
111
190
240
0.559
114
F
0.553
0.558
0.355
0.309
0.339
0.261
163
0.512
Gd
H0
0.644
0.63
0.76
0.782
0.738
0.865
0.666
0.687
0.56
0.597
0.711
0.787
0.729
0.783
0.5
0.6
N
75
77
83
89
59
69
76
85
77
A
R
4(3.68)
3(2.94)
5(4.31)
6 (4.75)
7(4.65)
8 (6.79)
7(4.31)
6(3.58)
5.75 (4.38)
113-119
147-155
107-155
95-131
180-198
200-252
155-251
114-150
0.72
5
113
147
107
115
190
240
163
114
F
0.688
0.784
0.494
0.399
0.376
0.354
0.796
0.811
Gd
0.487
0.362
0.657
0.724
0.668
0.779
0.357
0.33
H0
0.468
0.329
0.702
0.724
0.584
0.799
0.308
0.343
N
141
167
178
173
137
144
159
172
159
A
4(3.78)
3(2.95)
5(4.40)
5(4.44)
6(4.68)
7(6.41)
6(4.51)
5(3.44)
5.13 (4.32)
R
113-119
147-155
107-155
95-119
180-198
200-252
155-251
114-150
0.55
5
113
147
107
115
190
240
163
114
F
0.673
0.789
0.429
0.44
0.461
0.368
0.733
0.815
Gd
H.
0.508
0.357
0.684
0.697
0.651
0.769
0.445
0.322
0.524
0.344
0.761
0.758
0.605
0.842
0.419
0.326
N
84
90
92
90
76
76
86
89
85
A
4(3.56)
3(2.95)
5(4.22)
6(5.01)
6(4.69)
8(7.14)
7(3.77)
5(3.55)
5.50 (4.36)
R
113-119
147-155
107-155
95-131
180-198
200-252
155-251
114-150
0.55
S
113
113
107
115
188
240
163
114
F
0.711
0.779
0.564
0.355
0.393
0.338
0.87
0.807
Gd
H0
0.46
0.37
0.616
0.75
0.688
0.794
0.241
0.335
0.386
0.312
0.64
0.687
0.557
0.75
0.178
0.361
N
57
77
86
83
61
68
73
83
74
A
1(1.00)
2(1.96)
3(3.00)
3(3.00)
5(4.58)
4(3.94)
2(2.00)
1(1.00)
2.63 (2.56)
R
113
147-155
107-135
95-115
180-198
200-248
159-163
114
S
113
155
119
111
188,190
204
163
114
F
1
0.913
0.604
0.364
0.302
0.605
0.576
1
Gd
H.
0
0.161
0.56
0.667
0.752
0.583
0.492
0
0
0.143
0.514
0.6
0.698
0.439
0.424
0
N
53
63
72
70
43
57
66
72
62
A
4(3.93)
2(2.00)
5 (4.36)
6(5.32)
5(4.40)
7(6.38)
5(4.13)
4(2.75)
4.75 (4.16)
0.53
0.40
50
Locus
Sample Section
Ots-9
R
147
0.863
0.24
0.175
40
4(3.91)
3 (2.30)
147-155
A
R
One-lOS
One-I 03
200-252
240
0.306
0.788
155-251
114-146
163
114
0.871
0.757
0.412
0.378
31
37
0.925
0.144
0.15
40
6(5.36)
8(7.14)
6 (4.31)
5(4.40)
180-198
200-252
155-251
114-150
103
188
0.755
0.857
42
0.338
0.751
0.838
37
0.486
0.672
0.743
5(4.42)
5(4.93)
107-155
95-119
35
Mean
0.55
37
5.25 (4.60)
113
147
115
111
190
204,240
163
114
0.875
0.223
0.25
48
0.398
0.7
0.694
49
0.367
0.753
0.778
45
0.409
0.725
0.614
0.236
0.834
0,917
0.807
0.342
0.273
0.787
0.372
0.383
0.57
44
36
44
47
45
4(3.75)
3 (2.61)
147-155
4(3.92)
5(4.78)
5 (4.33)
4(3.56)
4.88 (4.09)
180-198
8 (5.50)
200-252
240
0.527
0.653
6(4.26)
107-135
155-251
114-150
A
R
113-119
113
147
107
0.71
0.467
0.449
69
0.888
0.205
0.211
76
0.576
0.606
0.595
79
95-131
95
0.406
0.691
0.696
69
A
R
4(3.61)
3(2.83)
4(3.98)
5(4.32)
113-119
107-135
95-131
S
F
113
147-155
147
0.821
0.31
0.357
42
3 (1.88)
147-155
N
Gd
H0
N
A
R
0.7
0.478
0.45
40
4(3.93)
113-119
114
43
37
34
35
4(3.66)
5 (3.99)
5 (4.52)
180-198
6 (5.24)
200-252
5(4.05)
107-135
1(1.00)
4(3.77)
5 (4.81)
113-119
147
147
107-135
95-131
95
0.343
0.737
0.714
63
H.
N
A
R
5
F
107
51
66
0.588
0.572
0.537
67
3(3.00)
1(1.00)
4(3.45)
4 (3.98)
113-119
147
147
107-135
95-131
113
0
0
107
0.5
5(3.97)
4 (2.75)
200-240
155-239
204
0.57
0.573
0.52
163
114
0.568
0.585
0.627
0.765
0.397
0.246
0.75
0406
59
50
61
63
67
3 (2.99)
188-194
3(2.88)
2 (2.00)
163-239
3 (2.48)
114-150
2.88 (2.72)
200-240
188
3 (2.95)
114-150
30
3.88 (3.36)
0.48
0.471
163
114
0.766
0.371
0.469
0.42
22
31
29
5 (4.33)
180-194
5 (3.86)
200-240
4 (3.16)
155-239
3(2.39)
3.88 (3.30)
188
240
163
114
5(4.39)
107-135
147
107
95-131
95
113
240
0.886
0.212
0.136
28
0.51
0.776
0.354
0.172
29
4(3.54)
S
4.38 (3.87)
5 (4.14)
180-194
147
113-119
4 (3.67)
114-150
39
25
1(1.00)
4(3.72)
0.52
114
33
A
R
N
0.311
0.349
43
0.765
0.362
0.429
34
188
0.634
0.697
4.63 (4.03)
163
0.519
0.582
0.741
27
0
0
33
155-251
69
0.625
0.548
0.357
111
1
0.4
0.51
240
0.523
0.632
0.727
22
188
0.48
0.642
0.52
0.516
0.649
0.656
32
0.558
0.601
0.5
26
Gd
H0
114-150
0.826
4(3.47)
Gd
4(3.58)
155-251
163
107
1
6(4.46)
200-252
0.686
0.488
0.528
0.627
0.639
36
113
6(5.33)
180-198
240
0.529
0.664
0.735
147
0.645
0.549
0.431
5(4.17)
190
0.971
0.058
0.029
34
F
63
0.486
0.612
0.676
113
S
56
62
111
0.724
0.457
0.448
29
A
R
0732
0.388
0.69
0.725
40
S
N
114
0.799
0.349
0.364
77
107
F
H.
163
0.659
0.513
0.381
190
0.435
0.626
0.613
0.616
0.577
0.558
95-131
95
0.448
0.678
0.655
29
Gd
T4-1 (age-1+)
One-I 02
0.605
0.576
0.535
43
S
F
Gd
H0
T4-O (age-0)
Ots-JO
180-198
F
N
T4
113-119
Omy-1046
95-131
S
1-10
MS2-1 (age-1+)
107
0.31
113
0.514
0.634
0.722
36
Gd
MS2-0 (age-0)
Ots-212
107-155
F
N
MS2
Ots-209
147-155
S
Gd
H0
T3a
113-119
114-150
51
Locus
Sample Section
Ots-9
0.70
0.479
0.36
Ots-209
N
25
A
R
F
Gd
H0
UT40
UT4-0 (age-0)
2(2.00)
2(2.00)
2.13 (2.09)
204-240
240
0.798
0.326
0.404
47
3 (3.00)
155-239
239
0.422
0.658
0.49
1(1.00)
188-194
51
52
49
2.13 (2.12)
3(2.96)
3 (3.00)
107-119
95-111
115
0.5
114
114
111
188
0
0.578
0.686
0.48
0.615
0.725
0.865
p.237
0.271
52
51
51
48
2(2.00)
1(1.00)
3(3.00)
3 (3.00)
2(2.00)
2(2.00)
3(3.00)
1(1.00)
113-119
147
147
107-119
95-111
188-194
114
114
1
0
1
0
0.31
0
115
111
188
0
0.471
0.613
0.882
0.647
0.528
0.647
0.912
0.166
0.176
0.781
0.353
0.438
15
18
17
17
17
16
17
18
17
A
2(1.81)
1(1.00)
3(2.95)
3 (3.00)
2(2.00)
2(2.00)
3(3.00)
1(1.00)
2.13 (2.09)
R
113-119
147
147
107-119
95-Ill
188-194
115
0.839
0,275
0.323
34
95
0.441
0.631
0.765
34
31
31
155-239
239
0.353
0.676
0.441
34
114
114
0.515
0.568
0.588
34
204-240
240
0.806
0.317
0.387
3(2.67)
4(3.91)
6(4.19)
147-155
107-135
95-131
113
0.967
0.067
0.067
0
N
S
113
F
N
0.963
0.073
0.074
27
A
R
4 (3.59)
113-119
1
1
0
0
188
5 (4.58)
180-198
7(5.27)
7(5.60)
200-252
240
0.403
0.742
0.833
72
155-255
7(5.55)
S
113
147
107
115
188
F
0.755
0.406
0.445
0.891
0.201
0.2
0.395
0.696
0.618
0.326
0.704
0.669
0.358
0.718
0.585
N
110
110
110
118
106
A
R
4 (3.62)
113-119
3(2.72)
4(3.96)
4(3.72)
5 (4.42)
147-155
95-115
180-198
115
188
0.373
0.684
0.701
67
0.385
0.726
0.574
5 (4.37)
200-252
240
0.39
0.736
0.854
61
41
Gd
H0
S
113
147
F
0.754
0.408
0.462
107-135
107
0.355
0.717
0.597
62
1
0
0.29
0
1
0
0
0.32
34
32
5 (3.46)
114-150
5.13 (4.16)
163
114
0.377
0.734
0.782
0.37
0.397
0.57
78
97
4 (3.24)
114-150
4.88 (4.36)
0.71
69
155-255
163
114
0.372
0.72
0.744
39
0.811
0.329
0.333
45
N
65
0.871
0.234
0.226
62
A
R
5
4(3.68)
3(2.25)
4(3.85)
6(4.77)
5(4.74)
7(6.15)
6(5.77)
5(3.68)
113-119
147-155
107-135
95-131
180-198
155-255
114-ISO
113
147
107
95
190
163
114
F
0.917
0.156
0.167
0.402
0.708
0.627
0.444
0.687
0.6
0.383
0.758
0.667
0.742
0.428
0.485
N
45
48
0.448
0.661
0.646
48
0.57
H.
0.756
0.408
0.422
200-252
240
0.419
0.757
0.806
51
45
31
30
33
41
A
R
2 (2.00)
113-119
1(1.00)
3(3.00)
6(5.20)
3.25 (3.02)
188-198
200-248
204
0.406
0.724
2 (2.00)
163-239
3(2.90)
107-119
5 (4.84)
95-119
4 (3.33)
147
147
Gd
H0
6d
MS4
38
147
147
F
H0
MS3-1 (age-1+)
32
1(1.00)
S
Gd
MS3-0 (age-0)
32
113-119
0
155-239
239
0.559
0.572
0.588
H0
MS30
28
2(1.74)
0
204-240
240
Gd
UT4-1 (age-l+)
0.46
31
113
A
R
Mean
0.705
0.429
0.538
33
0.964
0.07
0.071
42
N
One-i 03
0.719
0.439
0.313
0.531
32
F
H0
One-i 08
0.393
0.694
0.821
Omy-1046
0.565
0.632
0.774
S
Gd
One-I 02
Ots-10
0.625
0.565
Ots-212
0.676
0.489
0.382
34
1
S
113
F
0.841
0.269
Gd
115
95
188
1
0.5
0
0.619
0.412
0.725
0.682
0.477
0.57
55
3.25 (3.03)
114-150
163
114
0.692
0.429
0.828
0.301
0.44
52
Locus
M54-0 (age-0)
Mean
Ots-9
Ots-209
OIs-212
Omy-I 046
Ots-lO
One-I 02
One-I 08
One-103
1-10
0.273
0
0.585
0.846
0.494
0.713
0.36
0.269
N
88
94
94
91
85
80
86
93
89
A
R
2(1.99)
1(1.00)
3(3.00)
5(4.87)
4(3.24)
6(5.19)
2(2.00)
3(2.87)
3.13 (3.00)
113-119
147
107-119
95-119
188-198
200-248
163-239
114-150
S
113
147
115
95
188
204
163
114
F
0.882
1
0.458
0.988
0.788
0.404
0.735
0.845
Gd
H0
0.21
0
0.644
0.738
0.356
0.725
0.393
0.276
0.164
0
0.542
0.897
0.385
0.617
0.333
0.207
N
55
59
59
58
52
47
51
58
A
2(2.00)
1(1.00)
3(2.99)
5 (4.70)
4(3.44)
5(4.88)
2 (2.00)
3(2.96)
R
113-119
147
107-119
95-119
188-198
200-248
163-239
114-150
S
113
147
115
95
188
204
163
114
F
0.773
1
0.571
0.455
0.515
0.409
0.629
0.8
Gd
H.
0.357
0
0.57
0.703
0.6
0.731
0.474
0.345
0.455
0
0.657
0.758
0.667
0.848
0.4
0.371
N
33
35
35
33
33
33
35
35
Sample Section
0.42
55
MS4-1
(age-1+)
0.47
34